Monitoring and evaluating driving skills of a driver is of utmost importance, especially in view of the steep increase in accident across the globe in recent times. In the existing art, the driving skills have been evaluated by creating a data recording system and using machine learning to identify aggressiveness and skill of the driver. However, the existing methods relying on machine learning techniques require large training datasets. Majority of the other existing methods record vehicular data from On-board device (OBD), dedicated hardware, camera mounted on the vehicle and then uses multisensory data to characterize driver behavior. Further, the statistical analysis techniques have been proposed which uses standard deviation and mean of obtained acceleration for a driver's data. Further, some of the existing techniques compute driving risk by putting fixed thresholds in acceleration measurement. Thus, instances of such anomaly greatly affect a driver's risk score. Such thresholds are also subject to measurement errors. Further, there have been attempts made in the existing art to create driver profile from acceleration data using time domain and/or frequency domain analysis. Further, existing techniques rely on event-based approach for detecting driving behavior of a driver. The event-based approach involves analyzing data samples captured for shorter time period duration and identifies a specific event based upon the analysis, wherein the specific event facilitates to determine the driving behavior of the driver. However, the event-based approach is susceptible to errors since the analysis is performed on the data samples captured for shorter time period. For example, a driver may be ill while driving a vehicle for a specific time interval and hence drive the vehicle in abnormal condition. However, the existing event-based approach will still categorize the driving profile of the driver as ‘risky profile’ even if the driver is driving the vehicle in the normal condition for another time intervals.